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Found 74 papers, 38 papers with code
DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays
To address these issues, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children.
GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation
Secondly, we propose a novel inter-instance interaction using gate activation as a mask for self-attention.
Ranked #1 on
Video Instance Segmentation
on YouTube-VIS 2021
(using extra training data)
GenerateCT: Text-Guided 3D Chest CT Generation
We further investigate the potential of GenerateCT by training a model using generated CT volumes for multi-abnormality classification of chest CT volumes.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Link Prediction for Flow-Driven Spatial Networks
In this work, we focus on link prediction for flow-driven spatial networks, which are embedded in a Euclidean space and relate to physical exchange and transportation processes (e. g., blood flow in vessels or traffic flow in road networks).
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations
In this paper, we focus on improving ViT-AE (nicknamed ViT-AE++) for a more effective representation of 2D and 3D medical images.
Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis
In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores.
Approaching Peak Ground Truth
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images
We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings.
Topologically faithful image segmentation via induced matching of persistence barcodes
In this work, we propose the first topologically and feature-wise accurate metric and loss function for supervised image segmentation, which we term Betti matching.
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Focused Decoding Enables 3D Anatomical Detection by Transformers
To tackle this issue and facilitate the advent of medical Detection Transformers, we propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
Metrics reloaded: Pitfalls and recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models.
blob loss: instance imbalance aware loss functions for semantic segmentation
Blob loss is designed for semantic segmentation problems in which the instances are the connected components within a class.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels.
Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction.
Relationformer: A Unified Framework for Image-to-Graph Generation
We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients
Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem.
FedCostWAvg: A new averaging for better Federated Learning
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community.
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Differentiable Deconvolution for Improved Stroke Perfusion Analysis
We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach.
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations
Radiomic representations can quantify properties of regions of interest in medical image data.
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
Pyramid Architecture Search for Real-Time Image Deblurring
Multi-scale and multi-patch deep models have been shown effective in removing blurs of dynamic scenes.
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference
Existing medical image super-resolution methods rely on pairs of low- and high- resolution images to learn a mapping in a fully supervised manner.
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.
Automated Claustrum Segmentation in Human Brain MRI Using Deep Learning
In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function.
Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation
Tackling domain shifts in multi-centre and multi-vendor data sets remains challenging for cardiac image segmentation.
Face Super-Resolution Guided by 3D Facial Priors
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge.
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis.
Feedback Graph Attention Convolutional Network for Medical Image Enhancement
To well exploit global structural information and texture details, we propose a novel biomedical image enhancement network, named Feedback Graph Attention Convolutional Network (FB-GACN).
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns
In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e. g. a new centreor a new scanner.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing.
ModelHub.AI: Dissemination Platform for Deep Learning Models
Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others.
Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee
The existing implicit schemes are usually iterative and employ a general-purpose solver which may be sub-optimal for a specific class of PDEs.
Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation
Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming.
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.
Neural parameters estimation for brain tumor growth modeling
In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans.
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans
Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares.
Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net
Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Multi-Scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation
Secondly, we aggregate multi-scale Stack-Nets with different receptive fields to learn multi-scale contextual information of both large and small lesions.
Personalized Radiotherapy Design for Glioblastoma Using Mathematical Models, Multimodal Scans and Bayesian Inference
Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans.
Computational Engineering, Finance, and Science
Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks
The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC.
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images
In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans.
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Extracting the brain from images with strong pathologies, for example, the presence of a tumor or of a traumatic brain injury, is challenging.
Accelerated Reconstruction of Perfusion-Weighted MRI Enforcing Jointly Local and Nonlocal Spatio-temporal Constraints
Perfusion-weighted magnetic resonance imaging (MRI) is an imaging technique that allows one to measure tissue perfusion in an organ of interest through the injection of an intravascular paramagnetic contrast agent (CA).
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
A 3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from the HCC tumor segmentation.
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
In the first step, we train a FCN to segment the liver as ROI input for a second FCN.
Automatic Liver And Tumor Segmentation
Lesion Segmentation
+1
